The Effects of Social Networking Sites on Homestay Intention in Vietnam. a Thesis Submitted by Duchanh Tran Thi Under Supervisio

International Master of Business Administration Program (IMBA) National Taichung University of Education

The Effects of Social Networking Sites on Homestay Intention in Vietnam.

A thesis submitted by DucHanh Tran Thi Under supervision of Chih-Sung Lai Ph.D.

December, 2018

ACKNOWLEDGMENTS

This thesis was conducted and completed on time with many supports, good advice, and encouragement for me. Especially, I had the companionship with my advisors, friends, family and my own efforts during studying and performing this paper. I would like to deeply express my gratitude to everyone who has given me attention and help. First of all, I fully appreciate my advisor, Ph.D. Chih Sung Lai, who is willing provide best materials as a good reference. Under his thoughtful guidance and revision during the process of this paper, I have learnt more knowledge and gain my studying purpose when finishing my research. Secondly, I would like to send my thankfulness to the board of National Taichung University of Education because of offering me a good chance to study in a well-educated environment. I also sincerely thank other professors in IMBA department since their teaching lessons, advice and suggestions. Besides, I will inculcate enthusiastic caring and supporting from all staffs in IMBA department as well as my classmates. Finally, the profound care and durable encouragement from my family has been a source motivating me during studying program and through the research conducted. Therefore, I would like to dedicate my thesis to my family as my deep gratitude. And I do not forget my friends’ interests and help which companied with me.

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The Effects of Social Networking Sites on Homestay Intention in Vietnam.

Advisor: Chih-Sung Lai, Ph.D. International Master of Business Administration National Taichung University of Education

Student: DucHanh Tran Thi International Master of Business and Administration National Taichung University of Education

ABSTRACT Tourism industry is always considered a sustainable economic category to potentially invest and exploit which every nation in the world owns. And recently, homestay traveling has significantly contributed to the growth of the tourism industry in general. Because current trend of vacations that people have chosen is a holiday being close to nature and rusticity. However, people still can connect with others and share their enjoyfull traveling experience every moments by using social networking sites. This resarch was conducted to investigate the effects of four common social networking sites; Insatgram, Facebook, You tube, Google+ on homestay intetion in Vietnam. And the emprical research model was constructed with four variables; social networking sites, electronic word of mouth communication, information sharing, and home saty intention. Electronic word of mouth communication is considered as a platform on social networking sites for potential travelers with homestay intention engage. Information sharing plays the role as an informative and trustworthy source of social network users by sharing and being shared their homestay holiday profiles such as photos, videos, recommendations, and destinations. By utilizing the quantitative and deductive approach method, the data collected from 220 respondents by conducting the online and offline survey with questionnaire, this research was

analyzied to test resaerch hypotheses proposed with SPSS software. And the findings unraveled the relationship of four construsts built, and found out how social networking sites affects on homestay intention. Overall, this research provides an evidence to prove a positive influence of social networking on homestay intention. Key words: Social networking sites (SNSS), electronic word of mouth communication (EWOM), information sharing (IS), homestay intention (HI).

Tables of Contents

Chapter 1: Introduction ...... 1

1.1 Background and research rationale ...... 1

1.2 Research objectives ...... 6

1.3 Research questions...... 7

1.4 Research structure ...... 7

Chapter 2: Literature review ...... 10

2.1 Social networking sites...... 10

2.2 EWOM communication ...... 13

2.3 Information sharing ...... 15

2.4 Homestay Intention ...... 16

Chapter 3: Research Methodology...... 22

3.1 Research framework and hypotheses...... 22

3.2 Research design ...... 24

3.3 Sampling design...... 24

3.4 Data collection...... 25

3.5 Method of data analysis ...... 26

3.6 Questionnaire design ...... 26

Chapter 4: Research Analysis and analysis results...... 28

4.1 Encoding and Inputting data into SPSS ...... 28

4.2. Descriptive Analysis ...... 33

4.2.1 Demographic characteristics of respondents...... 33

4.2.2 Descriptive statistics items of variables...... 36

4.3 Reliability Analysis ...... 39

4.4 Exploratory factor analysis (EFA) ...... 41

4.4.1 Exploratory factor analysis (EFA) on SNSS ...... 42

4.4.2 Exploratory factor analysis (EFA) on EWOM ...... 43

4.4.3 Exploratory factor analysis (EFA) on IS ...... 43

4.4.4 Exploratory factor analysis (EFA) on HI ...... 44

4.5 Regression Analysis ...... 44

4.5.1 Regression Analysis of EWOM on SNSS ...... 45

4.5.2 Regression Analysis of IS on SNSS ...... 46

4.5.3 Regression Analysis of HI on SNSS ...... 47

4.5.4 Regression Analysis of HI on EWOM ...... 48

4.5.5 Regression Analysis of HI on IS ...... 48

4.6 Independent T-Test ...... 51

4.6.1 Independent T-Test of Gender ...... 51

4.6.2 Independent T-Test of Marital Status ...... 52

4.6.3 Independent T-Test of Like Homestay ...... 53

4.6.4 Independent T-Test of Efficiency of SNSS ...... 54

4.7 One way Anova Analysis ...... 55

4.7.1 One way Anova Analysis of Age ...... 56

4.7.2 One way Anova Analysis of Educational Background ...... 56

4.7.3 One way Anova Analysis of Occupation Status ...... 57

4.7.4 One way Anova Analysis of Monthly Income ...... 58

4.7.5 One way Anova Analysis of Living Place ...... 59

4.7.6 One way Anova Analysis of Frequency of Using SNSS...... 60

4.7.7 One way Anova Analysis of Which SNSS ...... 61

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4.7.8 One way Anova Analysis of Frequency of travelling ...... 62

Chapter 5: Conclusion and recommendations ...... 64

5.1 Mai findings and discussion ...... 64

5.1.1 Social networking sites ...... 65

5.1.2 Homestay intention ...... 66

5.1.3 Personal characters ...... 67

5.2 Contribution ...... 68

5.3 Limitations and recommendations for future researches ...... 69

5.4 Conclusion ...... 70

References ...... 72

APPENDIX ...... 77

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List of Tables

Table 4.1.1 Encoding System for Part 1 of the Questionnaire...... 28 Table 4.1.2 Encoding System for Part 2 of the Questionnaire...... 31

Table 4.2.1 Descriptive characteristics of respondents ...... 34 Table 4.2.2 Descriptive statistics of variables and items ...... 37 Table 4.3.1 Reliable Statistics ...... 40

Table 4.4.1 Exploratory factor analysis (EFA) on SNSS ...... 42

Table 4.4.2 Exploratory factor analysis (EFA) on EWOM ...... 43

Table 4.4.3 Exploratory factor analysis (EFA) on IS ...... 43 Table 4.4.4 Exploratory factor analysis (EFA) on HI ...... 44

Table 4.5.1 Single Regression Analysis of EWOM on SNSS ...... 45

Table 4.5.2 Single Regression Analysis of IS on SNSS ...... 46

Table 4.5.3 Single Regression Analysis of HI on SNSS ...... 47

Table 4.5.4 Single Regression Analysis of HI on EWOM ...... 48

Table 4.5.5 Single Regression Analysis of HI on IS ...... 49 Table 4.5.6 Summary of research hypotheses ...... 50

Table 4.6.1 Independent T-Test of Gender ...... 52

Table 4.6.2 Independent T-Test of Marital Status ...... 53

Table 4.6.3 Independent T-Test of Like Homestay ...... 54 Table 4.6.4 Independent T-Test of Efficiency of SNSS ...... 55

Table 4.7.1 Anova test of age ...... 56

Table 4.7.2 Anova test of Educational Background ...... 57

Table 4.7.3 Anova test of Occupation Status ...... 58

Table 4.7.4 Anova test of Monthly Income ...... 59

Table 4.7.5 Anova test of Living Place ...... 60

Table 4.7.6 Anova test of Frequency of Using SNSS ...... 60

Table 4.7.7 Anova test of Which SNSS ...... 61

Table 4.7.8 Anova test of frequency of travelling ...... 62

Table 4.7.9 Summary of personal demographic characteristics ...... 62

List of figures

Figure 1 International tourist from 1996 to 2016...... 2

Figure 2 Social network users from 2010 to 2021 ...... 4

Figure 3 Research design ...... 8

Figure 4 Instagram users from 2013 to 2018 ...... 11

Figure 5 Research framework ...... 22

Figure 6 Result of Research Model ...... 50

Chapter 1: Introduction

1.1 Background and research rationale

The countries which ranked in less developed ones, tourism has contributed effectively to socio-economic development (Sharpley, Richard Telfer, David J., 2014). Tourism has led the emerging sectors. In the economic growth, tourism industry complements significantly at the large portion. Besides, tourism industry strongly supports related tourism activities especially service and trade. With the trends of economic globalization, most countries have identified tourism industry as the economic driver. Apparently, most countries have carried out to promote their tourist destinations. And thanks to the development and advantages of high-information technology, image and information of destinations has been approached to international tourists easily and so fast. One of the rewards of high-information technology is social networking sites.

People can interact and exchange, and update any information via social networking sites. Even though, the plan of travelling will be implemented just few seconds.

According to Statistic, the number of international tourist arrivals worldwide reached

1.24 billion in 2016 shown in Figure 1 below. This is a certain witness to the explosive growth of tourism industry and it has proved that tourism is the largest and most powerful sectors in worldwide economic development (Scheyvens, 2002; Telfer & Sharpley, 2015). The data of

Statista, 2018; UNWTO, 2018 showed that tourism industry has been creasing steadily in two past decades and substantially in recent years. And the number of international tourists is also expected to exceed 1.8 billion, rise-up number, by 2030. The 7.61 trillion US dollars is the total contribution which travel and tourism had made to the global economy in 2016 (Statista, 2018).

It has significantly contributed for the growing of the national economy as well as the global

economy. Tourism industry indeed is an extremely important sector of economic country and worldwide.

Figure 1 International tourist from 1996 to 2016

Source: Statista (2018)

Together with global tourism increase, tourism industry of Vietnam has been a spectacular growth. The year of breakthrough of the tourism industry in Vietnam is 2017, and becomes one of highlights of Vietnam’s economic picture. In this year, Vietnam welcomed 12.9 million foreign tourists with about 30% increase compared to 2016 and served over 73 million domestic tourists. This is the first time that Vietnam was listed in countries with the fastest growth of tourism in the worldwide comparison. Vietnamese tourism has reached the sixth place in the list of top 10 tourist destinations with fastest growth rate in the world and the top in Asia in appreciation of The World Tourism Organization. This has established that Vietnam's tourism brand has increasingly strengthened its position in the international aren.

Vietnam with geographical advantages, a long coastline, stretching over 3,260 km from

North to South, is one of coastal countries. In addition, Vietnam owns the location of tropical area, the topography is dramatically varied with 3/4 emerald-green mountains, enrichment deltas, and tropical rainforests. Thence, Vietnam has an abundant climate differed between the south with two seasons (the rainy and the dry) the north with four seasons (spring, summer, autumn and winter). Vietnam geography has brought inestimable benefits and precious beautiful natural scenery. And a rich identity culture has been grown on life from long time. All above factors have produced strengths of tourism and this sector has been gradually specified the key and most growing economic sector of the country.

It must be conceded that homestay tourism should be in regards its contribution to the development of Vietnamese tourism industry. Because according to Vietnam Chamber of

Commerce and Industry, the statement of Secretary General of the World Tourism Organization

WTO, Mr. Taleb Rifai on the country’s potential developing homestay tourism was cited

“Vietnam has much potential of tourism homestay development”. In recent years, homestay tourism in Vietnam has attracted lots of attention and become popular towards domestic and international tourists. Vietnamese homestay tourism is believed that it is a really ideal tourism pattern for international visitors who are keen on travelling abroad to discover and get new experience through various cultural features of daily life. Including 54 folks, ethnic groups, from the North to the South, distinguishable and varied customs formed, and traditional festivals help homestay tourism attract domestic tourists moving to other regions to learn new things in their own country. So, it is certain that Vietnamese homestay tourism is a potential and promising category.

Before now, while travelling, people often took pictures or bought post cards of landscapes where they had chance to view beautiful natural scenery then sent to friends in

hometown. But nowadays, to do this task is extremely easy and quick in few seconds by a click the mouse or an Apps on smart phones thanks to social networking sites to connect people around the world.

Figure 2 Social network users from 2010 to 2021

Source: Statistic (2018)

And in this paper, the researcher focus on other social networking sites used much including Facebook, Instagram, YouTube, and Google+ to get the purpose of this research about

Vietnamese network users having access information from their social networking sites for homestay tourism. We all know that information content we are provided and update own ourselves from social networking sites is massive. Selecting necessary and useful information is a clever skill which any network users should have when they log in social networking sites every day.

In a small scope, this research just studies about information sharing of homestay tourism that network users share and get from their own sites. Photos, videos, recommendation, product and service of homestay destinations sharing is a real and lively information and it is also essential for promoting homestay tourism both hosts and visitors even though planners in this term. By using social networking sites, users have their own world to make, post and share their photos and videos and keep them as their own creative information or send to other people. In this paper, social networking sites were mentioned including Facebook, Instagram, You tube, and

Google+ to find out which is the most popular and well-used site related to getting travelling information. EWOM communication from social networking sites is another aspect which is examined how to influence on social networking users’ homestay intention.

Social networking site is a platform where network users have their freedom to raise questions, and help to reply inquiries of other fellows. They communicate by social networking sites to have information they need. From social networking sites interconnected users can make or get recommendations. Recommendations about tourism destinations, especially homestay in this case is very necessary for travelling planners. Photo and video sharing has become a popular activity of social networking users. Particularly, this act has been proposed as an efficient marketing tool toward tourism industry. Because information would encourage them with sufficient and useful details, the benefits that social networking sites bring to their users is massive information sharing. However, in its limitation, this research focused on adequate and essential information sharing that network users always create and gain related to homestay tourism, photos and video sharing, recommendation, product and service of destinations. With four social networking sites, Facebook, Instagram, you tube, and Google+ applied in this paper

to demonstrate their correlation of information sharing with EWOM communication, and their effects on homestay intention.

1.2 Research objectives

The trend of using social networking sites to provide effective information to whom in need as well as to connect with friends or relatives has been grown dramatically in daily Internet users. It is really helpful when people access their social working sites to get information about tourism, especially homestay in this case. Recently, tourists in particular prefer to make traveling plan by themselves. Homestay tourism in Vietnam has been listed in potential categories. It should be concerned and invested to develop in conservation.

The purpose of this paper is to investigate which effects of social networking sites lead to intention on homestay tourism in Vietnam. To better understand the homestay tourism perception via social networking sites of travelers is the key goal of this research. In that thank to social networking sites, how people who want to get homestay destinations will be easy to search information and then have intention on homestay tourism. Furthermore, information sharing from social networking sites considered reliable information source to provide who are its researchers are elucidated in this research.

The further target of this paper is to provide its contribution to later studies, position and importance of homestay tourism in tourism industry and to offer feasible thoughts for future researches on the related topic. From the findings of this paper, the conclusions are anticipated encouraging additional perceptive of the relation of homestay tourism and social networking sites to be marketing means to promote tourism destinations for tourists.

1.3 Research questions.

There are many previous researches about social networking sites and their impact on tourism industry. To get more adequate answers for the research purpose, these following questions are proposed to address the key factors of relationship between SNSS and homestay intention.

Central research question:

How do social networking sites affect homestay intention?

Sub-research questions:

Which is the most social networking site effective on homestay intention?

How does EWOM communication impact on homestay intention?

What exactly is the influence of information sharing on homestay intention?

Which factor of information sharing has a strong influence on homestay intention?

1.4 Research structure

As described in the chart below, the thesis will be divided into five chapters. The first chapter, introduction, is an overview of the study about homestay tourism and popular social networking sites in Vietnam. In Chapter 2, the literature of social networking sites; Instagram,

Facebook, you tube, and Google+, information sharing; photo-video, recommendation, destination, EWOM communication, and homestay intention will be reviewed. Chapter 3 discusses the methodology which the researcher applied to conduct the survey and collect the data is quantitative research and deductive approach. The information sharing and EWOM communication and social networking sites will be analyzed their effects on intention of homestay travelling in chapter 4. And the result of field of research will be presented in this

chapter. The final chapter has discussions about research main findings and recommendations to offer for future researches as well as homestay tourism.

The research process is shown as Figure 3.

Figure 3 Research design

Chapter 2: Literature review

2.1 Social networking sites

Social networking site is defined in term of web-based services that there exists three functions which in a bounded system each individual is allowed to construct a public or semi- public profile, that individual jointly collects a list of other users who they have the identified link to connect with, and within this system, their list connections only are viewed and browsed by other people who are created in that list (Boyd and Ellison, 2007). As the definition above, social networking site is understood as a website that provides people a social community to create their profile with data, photos, and personal information. Internet users consider social networking sites as an online community where the members can make connections with each other by using the services of websites to share their similar interests and mutual views.

In this paper, EWOM communication and information sharing about homestay tourism were tested to find out their influence through four following social networking sites common- used in Vietnam.

Instagram

Instagram was launched in 2010, known as a mobile application with the best function for users to take and share photos. Instagram has become faster popular social networking site because this special application was built for mobile usage (Neher, 2013). Image sharing on

Instagram is a typical example for visual communication. According to Jamieson (2007), information transferred through images created to communicate and express personal experiences, cultural and social backgrounds and attitudes to their viewer of the images. This is a proof of how images have influence than any texts.

Instagram has been grown its popularity by social network users because of photo sharing feature on mobile web. Social network users share real-time photos and short videos ultimately while this action is being taken place. Monthly active Instagram users had achieved the great number, 800 million in September 2017. Instagram is the most popular mobile photo sharing social network. The app allows users edit and share photos and videos. The statistic gives information that in March 2016, fashion brands using Instagram as their profile counted for 98 percent due to Instagram is a valuable social media marketing tool and because it owns visual nature and engagement rate of users is extremely high.

Figure 4 Instagram users from 2013 to 2018

Source: Statistic 2018

You tube

YouTube is a common public online video sharing website which is now operated as one of Google’s subsidiaries. User-generated videos can be watched, shared and discovered by a

huge number of internet users. People are provided YouTube as an online community for individuals. According to Lange (2007), YouTube has become a distribution platform for individuals to inform, interact, and connect with others. Thus, the host of user-generated content videos can be provided by YouTube and they are supported via socialization.

YouTube is considered as a video searching engine of social network users. And

YouTube has become popular video sharing website. Because YouTube users are allowed to upload, view, and share their favorite’s contents on videos, then other users can take part in these contents fully subscribed. During February 2017, the average minutely length of video uploaded to YouTube was more than 400 hours and daily content on YouTube being watched was one billion hours.

Facebook

Facebook is a very celebrated American online social networking service company. This website was launched in 2004, and Mark Zuckerberg is its founder. According to Statistic,

Facebook reached the monthly active users in number of more than 2.2 billion as of January

2018.

Facebook has had many various impacts on the social life and activity of people. People can use computers or mobile facilities to incessantly contact their friends, relatives and acquaintances all over the world when they have an own account to access to Facebook. Thank to Facebook, people can reunite their lost family members and friends through using its all features to connect others. People also have changed the ways to communicate because of impacts from Facebook's social. With broadcasting and sharing contents function to other users,

Facebook allows people engage and be engaged with all posts shared.

Google+

In June of 2011, Google+ was launched. It has many features including the ability to post photos, to update status to the stream or interest based communities. Moreover, it also has functions that different types of relationship are grouped, multi-person instant messages are easily sent. Hangouts are the feature for texting and video chatting. Especially, people can tag their locations. And Google+ allows its users have ability to edit photos as well as upload to private cloud-based albums.

Many Google properties provide and attach a Google+ user profile a visible account which is public. It comprises simple social networking services such as a profile photo, interests, and place used to live. In addition, many information like school history and previous works, even a place to post and updates these details in the profile. There are several identity service sections including contribution to other’s profiles that helps to link all properties through the web.

These optional sections with the link contribute to other social media accounts.

Social networking sites have been being used in increasing in daily day. Previously, it was for the youth but with the time every generation was found on these sites. So, marketers consider it as an effective promotional tool. And it has become a platform for consumers talking about products. Because from social networking sites, they can search any information they need.

And thanks to social networking sites, the connection with friends and family to get their opinion which is trusted as the scope of communication extends, and consumers also get to know the experiences of unknown people which diversify the EWOM communication. Thus, due to tremendous interest of people in social networking sites, marketers are finding it an effective way to engage consumers in EWOM communication due to the connectivity with the world by sitting at one place (Kozinets et al., 2010 & Jalilvand et al., 2011).

2.2 EWOM communication

No matter positive or negative description about product, service or even company generated by possible, genuine, or former customers has constituted EWOM communication.

And via the Internet, a plurality of people and institutions has made it available (Hennig-Thurau et al., 2004).

Seifert & Kwon (2015) found that EWOM plays an important role in brand value creation and brand trust. Consumers help marketers in contributing to their brand value by sharing and posting brand stories with other consumers. This way brand trust is also built among the consumers thus, influencing their purchase intention.

Online reviews, online recommendations have been belonged to EWOM communication.

When referring to online user-generated contents or online opinions, they are also put in EWOM communication term. With supporting of the advance of new technology tools, it has been drawn more attention (Serra Cantallops & Salvi, 2014). In the recent decades, thanks to the rapid development of Internet technology, people can use computers and their mobile phones to surf the Internet whenever, and wherever they want. As a result, any comments online about service, product, or brand from consumers are able to be posted and others can take their opinions as references.

Chevalier & Mayzlin, (2006) have cited that source of information from online user reviews is very useful and necessary for consumers. Because online user reviews are towards to consumers, it has become an important source. The other side, this kind of source also substitutes and complements other forms of communication of business-to-consumer. And quality of product is thanks to word of mouth communication being reached to consumers. In today’s online world, websites undeniably play an important role in EWOM communication.

When making a purchase decision, interpersonal influence of a consumer and EWOM communication are said to be the most essential information source (Litvin et al., 2008). These influences are particularly critical in the hospitality and tourism industry. Because this industry has intangible products, they are very difficult to measure before their consumption. Some researchers have already identified that the number of consumers depending on EWOM communication when making purchase decision are growing, such as reading book reviews, cosmetics reviews, or film reviews.

However, the online travel platform is wide and contains information from different categories, from service providers to customers, such as customer exchanges, blog publishing and online forums (Mack, Blose, & Bing, 2008). Although the quality of EWOM information and the reliability of sources may vary, the internet has dramatically changed word of mouth communication as consumers can exchange their opinions online.

EWOM communication plays an important role travel and tourism industry because consumers cannot assess products of tourism before purchasing. Therefore, EWOM communication turns to the information trusted and advised for later travelers because of others’ opinions.

2.3 Information sharing

Ko, Cho & Roberts, (2005) defined that information sharing is an exchange of significant intelligence based on social media in an instant manner. Any information or any contents are able to be created, shared and sought by social media users. Furthermore, social media assists communication between brands and users. Then the collaboration between them will be established (Sen & Lerman, 2007). Thus, information has been regarded as active products of social media users nowadays (Nov, Naaman, & Ye, 2010).

By collecting information as much as possible, then consumers make an informed purchasing decision. Because they prefer to conduct purchasing actions after they evaluate various available options and carry on a cost-benefit analysis, etc.

Nowadays, consumers desire to make a knowledgeable purchase decision by gathering sufficient information, evaluating diverse alternatives, carrying out cost-benefit analysis, and so on. To catch up views of consumer about products, marketers are required to be present on social media. And this has shaped a mutual two-way communication and users’ behaviors towards the brand and products (Najmi, Atefi, & Mirbagheri, 2012).

Consumers are getting a variety of brand-related social media websites or communities to express their opinions, and exchange information together. Thereby, this move allows the marketers have their indirect influence on attitudes of users towards their products and brands

(Hair, Clark, & Shapiro, 2010). Many studies have also revealed that people’s purchases and purchase experience are favorite topics to be discussed on social media. Consequently, the information has become a third party, and encouraged users have a positive influence on the brand (Chu, 2011).

2.4 Homestay Intention

In general knowledge, homestay program is divided into two particular forms; (1) accommodation for international students who take part in extended oversea program to learn local language as well as exchanged cultural and new experience, (2) accommodation for tourists who stay in a host family of a destination and experience local cultural, ecotourism encounter.

The second form will be examined in this paper. Lynch, McIntoch and Tucker (2009) cited that visitors can refer homestays as commercial homes whereby they pay to stay in private homes.

And homestays are where visitors or guests can interact with a host or family. In homestay tourism, visitors get involved with the host family all daily activities to gain experience from the local customs and lifestyle. This is a chance for visitors to spend time with the host family on discovering customs, values and culture, and then help them have opportunity to feel how savor of the rural life is.

Homestay is the process that hosts commercial their home as residential space for a profit-making. As a kind of accommodation, homestays hold the friendly scenery between a family member’s home and the entirely commercial surroundings which might be provided by hotels or motels (Lynch, Di Domenico, & Sweeney, 2007). Because of pursuing for novelty, personal service, and actual social interaction with the host family, travelers are appealed by homestays (Wang, 2007).

A unique local experience and opening interaction with the host family is the homestay’s offering to travelers. This is the chance to go through with new and unexploited places. The government has enabled not only to popularize new tourist destinations, but also the local community is provided alternative source of income. Spending time with the host family helps visitors of homestay tourism to get a chance discovering their customs, values and culture. Thus, with homestay tourism, visitors have the opportunities to enjoy the taste of rural life (Gangotia,

2013).

According to Espej et. al. (2008), purchase intention is identified when a consumer has behaviors regarding a future purchase decision. It is a kind of product which is bought for the next occasion. This expected outcome behavior comes from a purchase intention. In many marketing and economic researches, purchase intention has been extensively measured. They were studied in theses researches to help having prediction of the sales for new products

(Bemmaor, 1995).

In the research of Price and Feick (1984), consumers will have strong purchase intention if they are influenced by suggestions, opinions, and recommendations from their family, relatives, and friends. The amount of recommendations which it has generated can establish popularity of a product. And the information generated from consumers is effective in forming others purchase intention. Thus, a product in getting high involved consumers will be got more purchase intention.

2.5 Hypotheses derivations

The relationship between SNSS and EWOM communication

Consumers can engage in EWOM communication through social networking sites in varied degrees (Chu & Kim, 2011). If the consumers just view or read the post made by other people on social networking sites, they belong to the lowest level of engagement. The moderate level of engagement is when consumers contribute or participate in the conversations taking place online. The consumers are listed in the highest level of engagement if they create and share the user generated content i.e. photos, videos or recommendations online.

Consumers have considered social networking sites as consumers’ opinion platform and thus EWOM communication was generated. This platform helps consumers and marketers know the different dimensions of a product, for example – product reviews, discovering new trends and improving marketing campaigns, etc. In addition, EWOM communication helps in increasing the interest of consumers and providing them customized information. Other consumers also play an influent role and become an important promotional tool for the company (Arenas-Marquez et al.,

2014).

H1: SNSS will have a strong positive relationship with EWOM communication.

The relationship between SNSS and information sharing

To access the social networking sites, by using mobile devices, desktop computers, or notebooks, social network users can have support from advance of internet and high technologies.

And information and communication from these resources has become necessary tools for social network users engage the content created and information shared. The usage of various social networking in many various patterns of access methods has potentially impacted on information sharing.

The usage of the Internet thanks to advances of the technology has increased in recent years, and then has directed a revolution of communication (Moqbel, 2012). Millions users of social networking sites join daily online communication to create and share their information, user-created contents such as uploading photos, videos, making profiles, blogs. Originally, people use social networking sites for the entertainment purposes, however the way people communicate because the usage of Internet and information communication in increasing has been shifted, particularly in tourism industry (Assenov & Khurana, 2012).

H2: SNSS will have effects on information sharing.

The relationship between SNSS and homestay intention

Many firms have used social networking sites to communicate with consumers because the popular strategy in tourism industry. This kind of communication has a great influence on making consumers’ destination decision. Social networking sites have also provided the opportunity for this industry to connect and seek potential consumers in a quick and effective way. Marketers can promote and build the image of their brands thanks to the wide connection

of social networking sites to spread information and reach consumers around the world.

Moreover, it is very efficient for enterprises to aim at their consumers by utilizing registered data of consumers accessing social networking sites.

Social networking sites have become a significant and effective way of communication to marketing tourism products. According to Zeng (2013), marketers can make excellent plans to promote tourism products or service by using social media. Social networking sites help tourism companies make everything easier to target and reach their consumers because of the feature spreading the words (Murray & Waller, 2007).

According to Shen & Bissell (2013), social networking sites have offered a novel method to introduce brand-related content and generate exchanges with clients by creating consumer communications. The companies can use social networking sites in branding their images and products because of the impacts of social networking sites on influence consumer purchasing decision- making.

H3: SNSS will directly influence on homestay intention.

The relationship between EWOM communication and homestay intention

EWOM communication is generally accepted as a medium which plays a considerable role. This influences and forms attitudes of consumers and their behavioral intentions

(Bambauer-Sachse & Mangold, 2010). The findings of some prior studies have shown that

WOM communication is more prominent than the other causes (Bambauer-Sachse & Mangold,

2010) such as editorial recommendations, advertisements and so on because it is recognized to present relatively trustworthy information and has a vast persuasiveness though superior perceived reliability and dependability.

According to Bambauer-Sachse & Mangold, (2010), when searching for online product evaluations, consumers will discover different types of information. Gruen et al. (2006) have shown that customers may have higher credibility, interests, empathy, and relevancy and towards

EWOM communication than any sources of information on web created by marketers.

H4: EWOM communication will affect homestay intention.

The relationship between information sharing and homestay intention

Social media makes their users trust virtual brand communities. And because of this, social media helps consumers have a prosperous attitude towards virtual brand communities

(Hong, 2011). Using social media to promote marketing or advertising will affect consumers’ attitude towards the brand. When consumers have been affected by this kind of marketing or advertising and it will influence their purchase intention (MacKenzie, Lutz, & Belch, 1986).

Thanks to social networking sites, their users have found they have the most ideal communication environment. Sharing personal information and getting contact with other users are opportunities for them (Cerra, 2012). People can present themselves in a certain way for their own interests and entertainment and build up new relations via social networking sites.

H5: Information sharing will directly influence on homestay intention.

Chapter 3: Research Methodology

3.1 Research framework and hypotheses.

The model following was built on foundation of five above-presented hypotheses based on literature review and prior researches. These hypotheses were examined to find out the correlation among variables and then to test the basic idea of the research, effects of social networking sites on homestay intention. Thus, the variables of the constructs identified will be analyzed to address problems and objectives of research.

The research framework of this paper is as Figure 6.

Figure 5 Research framework

Hypotheses:

H1: SNSS will have a strong positive relationship with EWOM communication.

H2: SNSS will have effects on information sharing.

H3: SNSS will directly influence on homestay intention.

H4: EWOM communication will affect homestay intention.

H5: Information sharing will directly influence on homestay intention.

The personal demographic characters were beyond hypotheses of this research. In order to test information of respondens giving for 12 questions. The hypotheses following are proposed to understand if personal demographic character will have an influence on four variables constucted in empirical research model.

H6: Personal demographic characters will influence on SNSS, EWOM communication, IS and

HI.

H6.1 Gender factor will influence on SNSS, EWOM communication, IS and HI.

H6.2 Marital status factor will influence on SNSS, EWOM communication, IS and HI.

H6.3 Age factor will influence on SNSS, EWOM communication, IS and HI.

H6.4 Educational Background factor will influence on SNSS, EWOM communication, IS and HI.

H6.5 Occupation Status factor will influence on SNSS, EWOM communication, IS and HI.

H6.6 Monthly income factor will influence on SNSS, EWOM communication, IS and HI.

H6.7 Living place factor will influence on SNSS, EWOM communication, IS and HI.

H6.8 Frequency of using SNSS factor will influence on SNSS, EWOM communication, IS and

HI.

H6.9 Which SNSS factor will influence on SNSS, EWOM communication, IS and HI.

H6.10 Frequency of traveling factor will influence on SNSS, EWOM communication, IS and HI.

H6.11 Like homestay factor will influence on SNSS, EWOM communication, IS and HI.

H6.12 Efficiency of SNSS factor will influence on SNSS, EWOM communication, IS and HI.

3.2 Research design

This paper was conducted by utilizing the quantitative and deductive approach method.

When using quantitative methods, the main advantage is the statistics, conversion from the data to the numbers (Bryman and Bell, 2007).

The primary data of this paper was tested by using Statistical Package for the Social

Sciences (SPSS) software with the aim of finding out the correlation between SNSS and homestay intention. The questionnaire was performed based on four variables in constructs and six hypotheses identified of this paper. The questionnaire was sent to respondents and their responses were collected from August to September, 2018.

According to Selm and Jankowski (2006) it is very useful online questionnaire when the topic of Internet use because the research can reach a target population which has Internet experience. With the relevance to the topic of this paper, an online survey also has many advantages such as cost- reduction, time saving, target-accessibility as well as achievement of fast deployment and return times (Sepulveda, 2009).

3.3 Sampling design.

Because the purpose of the research is to study homestay intention which related to social networking sites, the expected respondents who use social networking sites, called social network users, was target population for this paper. Being sufficient to start analysis, the sample of the research at 220 participants took part in doing the questionnaire of the survey.

This research used a convenient sampling procedure for selecting participants because of the target population-based survey. Employing this technique would ensure the equality of representation of the variables.

3.4 Data collection

The data collected in this research includes the primary and the secondary data based on the qualitative approach. To gather the primary data, this thesis studied information background from social network users about homestay tourism to form the questionnaire. Moreover, the questions of the survey were structured based on the literature review of social networking sites, electronic word of mouth communication, and information sharing and homestay tourism. The information that the researcher assembles on his own by using interview, questionnaire, and tests becomes primary data for the research (Bryman and Bell, 2007). And the data was collected online and offline from the survey distributed to participants who use social network sites effectuated the questionnaire with the link via email, and social networking sites (Instagram,

Facebook, Google+).

Launching and collecting online data is the pest option for this paper because this method is low cost and practical. And this approach is easy to reach people online (Evans & Mathur,

2005). Other reason, participants of this paper are SNSS users, so they are flexible to choose the convenient time to fill the questionnaire.

Contrary to the primary data, the secondary data was collected in reference of literature, documents, and articles from previous researches. Reading texts of related literature review to get information of social networking sites, information sharing, EWOM communication and homestay tourism also are from recent published journals.

3.5 Method of data analysis

In this paper, data collected from online questionnaire survey was analyzed by applying

SPSS software with descriptive statistical analysis, reliability analysis, factor analysis, multiple regression analysis, one way Anova analysis, and Independent T-Test.

The author used the descriptive statistical analysis to describe the basis features of data gathered. This method would get the demographic summary of participants through numeral data.

The Cronbach’s Alpha was employed to test the reliability of the scales. With this method, each item of the scales would be found to reject or accept.

In the empirical of the research, all four constructs were measured by using multi-item scale. Factor analysis should be performed to explore the mutual correlation of selected items for constructs. Those constructs are Social Networking Sites, Electronic words of Mouth

Communication, Information Sharing, and Homestay Intention.

Single regression analysis was used to confirm a linear relationship between the dependent and the independent variables, and their influence of the conceptual research model.

To find out there exists any difference between each group of demographic characteristic of respondents on four variables constructed of this research, one way Anova and independent T- test method were utilized.

3.6 Questionnaire design

The questionnaire was performed on Drive of Google in Vietnamese language, comprising two parts: personal characters and homestay intention. The first part contains 12

questions to gather demographic data of the survey participants. The respondents were asked about personal details like age, gender, career, marital status, interests in social network and homestay tourism, etc. Followed by that, part 2 including 27 questions divided into 4 variables: information sharing (photos, videos, recommendation, destination), SNSS (Instagram, Facebook,

YouTube, Google+), EWOM communication, and homestay intention. The full version of the questionnaire is presented in Appendix.

In the second part of the questionnaire, 26 ascribed questions were appraised five-point

Likert scales used with subsequent categories: strongly disagree”, “disagree”, “neutral”, “agree” and “strongly agree” with “1” corresponding “strongly disagree” to 5 as “strongly agree”. Likert scale is the possible effective measurement for this paper because it is the most commonly used in survey research. Losby and Wetmore (2012) identified this scale as “ordered scale from which respondents choose one opinion that much in accordance with their attitude”. With this technique, the level of disagreement or agreement will be measured regarding the respondents’ view, belief, attitude and behavior with their precise answers to get an overall measurement about SNSS, EWOM communication, IS and HI.

Chapter 4: Research analysis and analysis results.

4.1 Encoding and inputting data into SPSS

The data was collected from online and offline survey and gathered through excel file.

Before putting transform collected data into analytical steps of SPSS, the dada should be encoded into a number. The questionnaire consists of two parts: the first part with 7 questions identifying demographic of respondents such as gender, marital status, age, education background, occupation status, monthly income, place living, and 5 another questions relating to interests, frequency of using SNSS and travelling. The second part was constructed as the scale items of the research with 27 questions, and divided into 4 variables: Social Networking Sites

(SNSS), Electronic Word-of-Mouth Communication, (EWOM), Information Sharing (IS), and

Homestay Intention (HI).

Table 4.1.1 and table 4.1.2 below shows the encoding system to the part one and part two of this study.

Table 4.1.1 Encoding System for Part 1 of the Questionnaire

Question Content Variable Response Encoding

1 Gender Male 1

Female 2

2 Marital status Unmarried 1

Married 2

3 Age under 20 1

21-30 2

31-40 3

41-50 4

Above 51 5

4 Educational Junior high school 1

Background Senior high school 2

College 3 University 4

Master 5

Doctor 6

5 Occupation Student 1

Status Part-time work 2

Full-time work 3 Self-employed 4

Other 5

6 Monthly Under 6 millions 1

Income (VND) 6 – 10 millions 2

11 – 15 millions 3

16 – 20 millions 4

21 – 25 millions 5

Above 25 millions 6

7 Living place Metropolis 1

City 2

Town 3

Countryside 4

Coastal areas 5

8 How frequently do Frequency of Daily 1

you use the social using SNSS 2 – 6 times a week 2 network sites? Once a week 3

Once a month 4

Other 5

9 Which social Which SNSS Facebook 1

network sites do Instagram 2 you most You tube 3 commonly use to Google+ 4 get information about travelling? Other 5

10 How often do you Frequency of 0-3 times 1

go travelling per travelling 4 – 6 times 2 year? 7 – 10 times 3

Over 10 times 4

11 Do you like Like Homestay Yes 1

homestay No 2 travelling?

12 Do you think it is Efficiency of Yes 1

efficient when SNSS No 2 booking homestay vacation on social networking sites?

Table 4.1.2 Encoding System for Part 2 of the Questionnaire

I. Social networking sites SNSS

1. SNSS have enabled me to create, publish, form groups, and share homestay SNSS1 travelling interests, exchange opinions or suggestions.

2. SNSS provide platforms for me to gratify my status and information seeking SNSS2 needs by sharing information.

3.Thanks to SNSS, I can effectively collect full information of homestay SNSS3 destinations I need.

4. Using social networking sites help me make decisions better before purchasing SNSS4 homestay travelling.

5. SNSS have become popular platforms for me to share my photos and videos of SNSS5 my travelling experience.

6. SNSS play an important role in information diffusion among tourist and SNSS6 influence my intention.

7. SSNS is an effective informative source for supporting me in planning SNSS7 homestay vacations.

8. SNSS enable me to stay in touch with people linked to access homestay sites. SNSS8

EWOM communication EWOM

1. Like other consumers, I can engage in EWOM communication through social EWOM1 networking sites in varied degrees.

2.EWOM communication on social networking sites helps in increasing interest of EWOM2 my homestay travelling and providing me customized information.

3.I considered EWOM communication as a type of social influence that affects my EWOM3 belief, attitude, and homestay intention.

4.EWOM communication has a positive impact on my purchasing intention, EWOM4 especially homestay travelling products.

5.I trust EWOM communication platform as a consultant before having homestay EWOM5 intention.

6.Using the EWOM communication, I can get much more interaction with other EWOM6 consumers and get faster response about the homestay product information.

II. Information Sharing IS

1. I trust information sharing of homestay travelling on SNSS shared by my IS1 relatives and friends.

2. I consider information sharing of homestay travelling on SNSS from my IS2 relatives and friends to plan for my future vacations.

3. I prefer to make an informed purchase decision by collecting as much IS3 information as I can get through social networking sites.

4.Video-sharing on social networking sites, make me want to visit homestay I IS4 have already seen in those videos.

5. Shared photos on social networking sites, make me want to visit homestay I IS5 have already seen in those photos.

6. I can get several options of homestay destinations shared by other users on IS6 SNSS.

7. Using social networking sites to get recommendations from acquaintances are IS7 affecting my decision-making on homestay travelling.

III. Homestay Intention HI

1. Using social networking sites of homestay help me make decisions better before HI1 booking homestay holiday.

2. Using social networking sites of homestay increase my interest in homestay HI2 travelling.

3. I will choose homestay for next holiday after reviewing a good homestay site. HI3

4. I am very likely to purchase homestay products recommended by my friends on HI4 social networking sites

5. I intend to purchase homestay travelling on social networking sites, I follow. HI5

6. I expect to purchase homestay products on social networking sites, I follow. HI6

4.2 Descriptive Analysis

4.2.1 Demographic characteristics of respondents.

In this research, 220 respondents are considered SNSS users in accodance with the target population of this paper. Among all the respondents, female was 134 respondents, 60.9%, more than respondents of male with the number 86 accounted for 39.1%. There were 55.9% unmarried respondents, and married less than with 44.1%. The greater part of respondents were between the ages 21 to 30 with a total of 55.9%. There were 2 above-51-age respondents, only 0.9% in this paper. Most of participants were well educated with a total of 42.7% university, 19.1% college, and 17.7% master. The significant number of occupation status was grouped in the category

“full-work” with 47.7%. Besides, the next major group was self-employed with 48 respondents.

Both of groups in total presented almost 70% of the sample. About the “monthly income” category, in this survey, percentage divided by numbers, there was no pointed group, more than other groups comprising 6 – 10 millions, 26.4% and 11 – 15 millions, 23.2%. Because this topic is relevant to living place of respondents, so this paper got 116 respondents in metropolis group, accounting for 52.7%, and in city with a total of 20.9%. The questions of frequency of using

SNSS and which SNSS was put into the questionnaire of demographic profile to support more clearly how SNSS has effected on homestay intention of respondents. There were 182 respondents using SNSS in group “daily”, accounting for 82.7%. And the most-used SNSS was

Facebook with a total of 63.2%. The category for the question of frequency of travelling per year was “0 -3 times” presented 65.9%, and 24.5 % for “4-6 times”. There were 204 respondents giving “Yes” answers with the question “Do you like homestay travelling?” with a total of 92.7%.

The agreement of respondents for the question “Do you think it is efficient when booking homestay vacation on social networking sites?” was 206 answers, presented 93.6%.

Table 4.2.1 Descriptive characteristics of respondents

Variable Response Frequency Percentage (%)

Gender Male 86 39.1

Female 134 60.9

Marital Status Unmarried 123 55.9

Married 97 44.1

Age Under 20 16 7.3

20-30 120 54.5

31-40 66 30.0

41-50 16 7.3

Above 51 2 0.9

Education Background Junior high school 9 4.1

Senior high school 36 16.4

College 42 19.1

University 94 42.7

Master 39 17.7

Doctor 0 0

Occupation Status Student 26 11.8

Part-time work 27 12.3

Full-time work 105 47.7

Self-employed 48 21.8

Other 14 6.4

Monthly Income Under 6 millions 32 14.5

6 – 10 millions 58 26.4

11 – 15 millions 51 23.2

16 – 20 millions 35 15.9

21– 25 millions 16 7.3

Above 25 millions 28 12.7

Living Place Metropolis 116 52.7

City 46 20.9

Town 30 13.6

Countryside 24 10.9

Coastal areas 4 1.8

Frequency of using SNSS Daily 182 82.7

2 – 6 times a week 30 13.6

Once a week 4 1.8

Once a month 3 1.4

Other 1 0.5

Which SNSS Facebook 139 63.2

Instagram 20 9.1

You tube 21 9.5

+ Google 33 15.0

Other 7 3.2

Frequency of travelling 0-3 times 145 65.9

4 – 6 times 54 24.5

7 – 10 times 15 6.8

Over 10 times 6 2.7

Like Homestay Yes 204 92.7

No 16 7.3

Efficient of SNSS Yes 206 93.6

No 14 6.4

Source: Drawn by the author from SPSS input data.

4.2.2 Descriptive statistics items of variables

The descriptive statistics of all items in four variables of the research with the

quantitative using 5-point Likert scale are shown at the table 4.2.2 below. The total 27 scale

items were divided into 4 variables: 8 items of Social Networking Sites, 6 items of Electronic

Word of Mouth Communication, 7 items of Information Sharing, and 6 items of Homestay

Intention.

In the table 4.2.2, all items have high mean scores, greater than 3.8. This result explained

that the agreement of respondents on measurement of items was in high levels. The very high

mean was 4.03 for the statement “SNSS have become popular platforms for me to share my

photos and videos of my travelling experience.” This number indicated that all most the

respondents have a positive evaluation of affection of SNSS for sharing their travelling

activities. In addition, 4 items in Information Sharing also got the mean over 4.0. This

interesting result showed that the participants of this research are interested in information

source shared on SNSS. For the last constructs, Homestay Intention towards the purpose of

the paper, all 6 items have mean scores over 3.9. It means the respondents tend to have a high

perception of agreement with these items.

Table 4.2.2 Descriptive statistics of variables and items

Variables / items Code Mean Std. Dev I. Social networking sites SNSS

1. SNSS have enabled me to create, publish, form SNSS1 3.93 0.984 groups, and share homestay travelling interests, exchange opinions or suggestions.

2. SNSS provide platforms for me to gratify my SNSS2 3.93 1.049 status and information seeking needs by sharing information.

3.Thanks to SNSS, I can effectively collect full SNSS3 3.85 1.106 information of homestay destinations I need.

4. Using social networking sites help me make SNSS4 3.86 1.095 decisions better before purchasing homestay travelling.

5. SNSS have become popular platforms for me to share SNSS5 4.03 1.077 my photos and videos of my travelling experience.

6. SNSS play an important role in information diffusion SNSS6 3.98 1.112 among tourist and influence my intention.

7. SSNS is an effective informative source for supporting SNSS7 3.92 1.076 me in planning homestay vacations.

8. SNSS enable me to stay in touch with people SNSS8 3.98 1.087 linked to access homestay sites.

II. EWOM communication EWOM

1. Like other consumers, I can engage in EWOM EWOM1 3.82 1.016 communication through social networking sites in varied degrees.

2.EWOM communication on social networking sites EWOM2 3.98 1.060 helps in increasing interest of my homestay travelling

and providing me customized information.

3.I considered EWOM communication as a type of EWOM3 3.88 1.029 social influence that affects my belief, attitude, and homestay intention.

4.EWOM communication has a positive impact on EWOM4 3.83 1.070 my purchasing intention, especially homestay travelling products.

5.I trust EWOM communication platform as a EWOM5 3.83 1.109 consultant before having homestay intention.

6.Using the EWOM communication, I can get much EWOM6 3.81 1.114 more interaction with other consumers and get faster response about the homestay product information.

III. Information Sharing IS

1. I trust information sharing of homestay travelling IS1 3.83 1.067 on SNSS shared by my relatives and friends.

2. I consider information sharing of homestay IS2 3.98 1.016 travelling on SNSS from my relatives and friends to plan for my future vacations.

3. I prefer to make an informed purchase decision by IS3 4.00 1.053 collecting as much information as I can get through social networking sites.

4.Video-sharing on social networking sites, make me IS4 4.04 1.026 want to visit homestay I have already seen in those videos.

5. Shared photos on social networking sites make me IS5 4.01 1.042 want to visit homestay I have already seen in those photos.

6. I can get several options of homestay destinations IS6 4.00 1.079

shared by other users on SNSS.

7. Using social networking sites to get IS7 4.03 1.057 recommendations from acquaintances are affecting my decision-making on homestay travelling.

IV. Homestay Intention HI

1. Using social networking sites of homestay help me HI1 3.94 1.047 make decisions better before booking homestay holiday.

2. Using social networking sites of homestay increase HI2 3.98 1.029 my interest in homestay travelling.

3. I will choose homestay for next holiday after HI3 3.95 1.028 reviewing a good homestay site.

4. I am very likely to purchase homestay products HI4 3.98 1.009 recommended by my friends on social networking sites

5. I intend to purchase homestay travelling on social HI5 3.92 1.026 networking sites, I follow.

6. I expect to purchase homestay products on social HI6 3.90 1.049 networking sites, I follow.

Source: Drawn by the author from SPSS input data.

4.3 Reliability Analysis

In this study, the Cronbach's Coefficient Alpha correlation coefficients were used to assess the reliability of each scale. Reliability of a construct is measured by examining the indicator reliability and composite reliability (Bagozzi & Yi, 2012). Therefore, this paper used the Cronbach's method to test all the items constructed internal consistency. Using the

Cronbach's Coefficient Alpha helped to determine if the scaled items were really meaningful

from the survey and avoids deceptive data. According to Cronbach & Shavelson (2004), alpha

values are considered appropriate in exploratory research between 0.60 and 0.70, and above 0.70

in advanced research. Thus, for this study, the alpha coefficient of 0.6 or greater is acceptable.

When evaluating the suitability of each item, items that have an item-total correlation

greater than or equal to 0.3 are considered reliable items. Items with a modulus of less than 0.3

will be removed from the scale. Corrected Item-Total Correlation that are small (less than 0.3);

Criteria for scale selection when Cronbach's Alpha if Item deleted is greater than Cronbach's

Alpha (Nunally & Burnstein 1994).

The Cronbach’s Alpha of all items is 0.987. The result in table 4.3.1 showed that all

questions of the scale items in this survey are highly reliable, and none of them was removed in

further analysis steps.

Table 4.3.1 Reliable Statistics

Code Scale Mean if Scale Variance Corrected Item- Cronbach's Item Deleted if Item Deleted Total Alpha if Item Correlation Deleted SNSS The Cronbach's Alpha: 0.966 N of Items:8 SNSS1 27.54 47.281 .844 .963 SNSS2 27.55 46.295 .860 .962 SNSS3 27.62 45.551 .863 .962 SNSS4 27.61 45.544 .874 .961 SNSS5 27.45 45.700 .880 .961 SNSS6 27.50 45.347 .873 .961 SNSS7 27.55 45.746 .877 .961 SNSS8 27.50 45.758 .865 .962

EWOM The Cronbach's Alpha: 0.960 N of Items:6 EWOM1 19.33 24.513 .860 .954 EWOM2 19.17 23.885 .888 .951 EWOM3 19.27 24.108 .895 .951 EWOM4 19.32 23.853 .880 .952 EWOM5 19.32 23.599 .870 .953 EWOM6 19.34 23.678 .856 .955 IS The Cronbach's Alpha: 0.963 N of Items:7 IS1 24.06 32.873 .823 .960 IS2 23.91 33.133 .849 .958 IS3 23.89 32.494 .873 .957 IS4 23.85 32.512 .899 .955 IS5 23.88 32.452 .888 .955 IS6 23.90 32.222 .874 .957 IS7 23.86 32.460 .873 .957 HI The Cronbach's Alpha: 0.962 N of Items:6 HI1 19.73 22.674 .844 .959 HI2 19.69 22.435 .892 .953 HI3 19.72 22.640 .868 .956 HI4 19.69 22.516 .904 .952 HI5 19.75 22.535 .883 .954 HI6 19.76 22.327 .883 .954

Source: Drawn by the author from SPSS input data.

4.4 Exploratory factor analysis (EFA)

After reliability analysis was tested, there are 27 items of four above constructs are highly

qualified to be entered the factor analysis. In the analysis of the EFA, a number of criteria are

often interested in including:

To determine the suitability of factor analysis, The Kaiser-Meyer-Olkin (KMO) is an index was used. The value of KMO (between 0.5 and 1) is sufficient and appropriate for the factor analysis. If the KMO index is less than 0.5, it is not appropriate for the data.

Bartlett's test with Sig. value is used to determine whether the observed variables in the correlation coefficient. To apply factor analysis, the condition should be enough that the observed variables reflect different aspects of the same factor that must be correlated. Bartlett's test was statistically significant (sig Bartlett's Test <0.05), indicating that the observed variables were correlated in the factor.

4.4.1 Exploratory factor analysis (EFA) on SNSS

The result from Table 4.4.1 presented the coefficient KMO = 0.945, is close to 1. So, the factor analysis is sufficiently appropriate. And Bartlett’s Test with Sig. = 0.000 < 0.05 showed that there was a correlation between items in SNSS variables. Table 4.4.1 Exploratory factor analysis (EFA) on SNSS

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Cumulative Total % of Cumulative Variance % Variance % 1 6.472 80.894 80.894 6.472 80.894 80.894 2 .330 4.130 85.024 3 .304 3.801 88.824 4 .267 3.341 92.166 5 .189 2.366 94.532 6 .162 2.022 96.554 7 .141 1.767 98.321 8 .134 1.679 100.000

Source: Drawn by the author from SPSS input data.

4.4.2 Exploratory factor analysis (EFA) on EWOM

The result from table 4.4.2 presented the coefficient KMO = 0.933, is close to 1. So, the factor analysis is sufficiently appropriate. And Bartlett’s Test with Sig = 0.000 < 0.05 showed that there was a correlation between items in EWOM variables. Table 4.4.2 Exploratory factor analysis (EFA) on EWOM

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Cumulative Total % of Cumulative Variance % Variance % 1 5.015 83.583 83.583 5.015 83.583 83.583 2 .292 4.859 88.442 3 .205 3.418 91.860 4 .187 3.115 94.975 5 .160 2.661 97.635 6 .142 2.365 100.000

Source: Drawn by the author from SPSS input data.

4.4.3 Exploratory factor analysis (EFA) on IS

The result from table 4.4.3 presented the coefficient KMO = 0.923, is close to 1. So, the factor analysis is sufficiently appropriate. And Bartlett’s Test with Sig = 0.000 < 0.05 showed that there was a correlation between items in IS variables. Table 4.4.3 Exploratory factor analysis (EFA) on IS

Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Cumulative Total % of Cumulative Variance % Variance % 1 5.730 81.852 81.852 5.730 81.852 81.852 2 .358 5.117 86.970 3 .250 3.566 90.535 4 .220 3.145 93.681 5 .196 2.794 96.474

6 .164 2.343 98.817 7 .083 1.183 100.000

Source: Drawn by the author from SPSS input data.

4.4.4 Exploratory factor analysis (EFA) on HI

The result from table 4.4.4 presented the coefficient KMO = 0.911, is close to 1. So, the factor analysis is sufficiently appropriate. And Bartlett’s Test with Sig = 0.000 < 0.05 showed that there was a correlation between items in HI variables. Table 4.4.4 Exploratory factor analysis (EFA) on HI

Source: Drawn by the author from SPSS input data.

4.5 Single Regression Analysis

After applying the reliability and factor analysis for four variables of research model, total 27 items were entered the regression analysis to test hypotheses of this research. This step was used to find out the effect of independent variables on dependent variables to get the linear relationship between SNSS, EWOM, IS, and HI. This test explored how much HI would be affected when three proposed SNSS, EWOM, and IS changed each of unit.

In this section, the Adjusted R Square coefficient was reviewed to evaluate the suitability of the study and the degree of influence of independent variables on the dependent variables.

This value about more than 50% is a feasible study. And the F coefficient, if Sig <0.05, in the

ANOVA also indicated the linear regression model constructed is consistent with the overall.

Then the values in Coefficient table also were examined including the value of t, Sig, and Beta

(β). If Sig. is less than or equal to 0.05, meaning that the variable is significant in the model, whereas Sig. is greater than 0.05, and the independent variable needs to be removed. In all regression coefficients, independent variables with the largest Beta that most affects the change of dependent variable.

4.5.1 Regression Analysis of EWOM on SNSS

The table 4.5.1 showed the results with the value of the Adjusted R Square 0.757, the proportion of this dependent variable. This indicated that EWOM variable would have 75.5% influent change by the change of independent SNSS variable. There was a significant regression relation between SNSS and EWOM because of the value of F = 682.988, and p = 0.000 < 0.05.

The β coefficient of SNSS value is 0.871, t =26.134, Sig. = 0.000 <0.05. This means the level of

EWOM will increase 0.871 units when SNSS increase one unit. The constant is 0.401 and Sig. =

0.000 < 0.05. That means there is an intercept. Therefore, the linear relation existed to support the influence of SNSS on EWOM, and H1 would be accepted with the regression equation:

EWOM = 0.401 + 0.871*SNSS

Table 4.5.1 Single Regression Analysis of EWOM on SNSS

Independent Dependent Variable -EWOM Unstandardized Standardized Coefficients B Coefficients t Sig. Beta

(Constant) .401 2.941 .004 SNSS .879 .871 26.134 .000 R2 .758 Adjust R2 .757 F 682.988 Sig. .000b St Error .4805423

Source: Drawn by the author from SPSS input data.

4.5.2 Regression Analysis of IS on SNSS

The table 4.5.2 showed the results with the value of the Adjusted R Square 0.817, the proportion of this dependent variable. This indicated that IS variable would have 81.7% influent change by the change of independent SNSS variable. There was a significant regression relation between SNSS and IS because of the value of F =981.338, and p = 0.000 < 0.05. The β coefficient of SNSS value is 0.905, t =31.326, Sig = 0.000 < 0.05. This means the level of IS will increase 0.905 unit when SNSS increase one unit. The constant is 0.488 and Sig. = 0.000 < 0.05.

That means there is an intercept. Therefore, the linear relation existed to support the influence of

SNSS on IS, and H2 would be accepted with the regression equation:

IS= 0.488 + 0.905*SNSS

Table 4.5.2 Single Regression Analysis of IS on SNSS

Independent Dependent Variable - IS Unstandardized Standardized Coefficients B Coefficients t Sig. Beta (Constant) .488 4.251 .000 SNSS .889 .905 31.326 .000 R2 .818 Adjust R2 .817 F 981.338 Sig. .000b

St Error .4053366

Source: Drawn by the author from SPSS input data.

4.5.3 Regression Analysis of HI on SNSS

The table 4.5.3 showed the results with the value of the Adjusted R Square 0.767, the proportion of this dependent variable. This indicated that HI variable would have 76.7% influent change by the change of independent SNSS variable. There was a significant regression relation between SNSS and HI because of the value of F =721.649, and p = 0.000 < 0.05. The β coefficient of SNSS value is 0.876, t =26.864, Sig = 0.000 <0.05. This means the level of HI will increase 0.876 units if SNSS increases one unit. The constant is 0.568 and Sig. = 0.000 < 0.05.

That means there is an intercept. Therefore, the linear relation existed to support the influence of

SNSS on HI, and H3 would be accepted with the regression equation:

HI= 0.568 + 0.876*SNSS

Table 4.5.3 Single Regression Analysis of HI on SNSS

Independent Dependent Variable -HI Unstandardized Standardized Coefficients B Coefficients t Sig. Beta (Constant) .568 4.393 .000 SNSS .858 .876 26.864 .000 R2 .768 Adjust R2 .767 F 721.649 Sig. .000b St Error .4564812

Source: Drawn by the author from SPSS input data.

4.5.4 Regression Analysis of HI on EWOM

The table 4.5.4 showed the results with the value of the Adjusted R Square 0.752, the proportion of this dependent variable. This indicated that HI variable would have 75.2% influent change by the change of independent SNSS variable. There was a significant regression relation between EWOM and HI because of the value of F =665.258, and p = 0.000 < 0.05. The β coefficient of EWOM value is 0.868, t =25.793, Sig = 0.000 <0.05. This means the level of HI will increase 0.868 units if EWOM increases one unit. The constant is 0.696 and Sig. = 0.000 <

0.05. That means there is an intercept. Therefore, the linear relation existed to support the influence of EWOM on HI, and H4 would be accepted with the regression equation:

HI= 0.696 + 0.868*EWOM

Table 4.5.4 Single Regression Analysis of HI on EWOM

Independent Dependent Variable -HI Unstandardized Standardized Coefficients B Coefficients t Sig. Beta (Constant) .696 5.361 .000 EWOM .842 .868 25.793 .000 R2 .753 Adjust R2 .752 F 665.258 Sig. .000b St Error .4708275

Source: Drawn by the author from SPSS input data.

4.5.5 Regression Analysis of HI on IS

The table 4.5.5 showed the results with the value of the Adjusted R Square 0.789, the proportion of this dependent variable. This indicated that HI variable would have 78.9 % influent change by the change of independent IS variable. There was a significant regression relation

between IS and HI because of the value of F =818,483 and p = 0.000 < 0.05. The coefficient standardized β coefficient of IS value is 0.889, t =28,609 Sig = 0.000 < 0.05. This means the level of HI will increase 0.889 unit when IS increases one unit. The constant is 0.415 and Sig. =

0.000 < 0.05. That means there is an intercept. Therefore, the linear relation existed to support the influence of IS on HI, and H5 would be accepted with the regression equation:

HI= 0.415 + 0.889*IS

Table 4.5.5 Single Regression Analysis of HI on IS

Independent Dependent Variable -HI Unstandardized Standardized Coefficients B Coefficients t Sig. Beta (Constant) .415 3.275 .001 IS .886 .889 28.609 .000 R2 .790 Adjust R2 .789 F 818.483 Sig. 000b St Error .4346348

Source: Drawn by the author from SPSS input data.

Based on the result of the above analysis, the relationship between variables of the research framework could be illustrated as figure 6. Furthermore, the top five hypotheses were all supported as Table 4.5.6.

Figure 6 Result of Research Model

Table 4.5.6 Summary of research hypotheses

Hypotheses of Research Model Status

H1: SNSS will have a strong positive relationship with EWOM communication. Supported

H2: SNSS will have effects on information sharing. Supported

H3: SNSS will directly influence on homestay intention. Supported

H4: EWOM communication will affect homestay intention. Supported

H5: Information sharing will directly influence on homestay intention. Supported

4.6 Independent T-Test

In this sector, Independent T-Test was applied to test whether there was any significant difference in the case two-variable-feature groups of respondents. Therefore, four these groups consisting of gender, marital status, like homestay, and efficiency of SNSS were analyzed to compare their means in four variables: Social networking Sites, Electronic Words of Mouth,

Information sharing, and Homestay Intention.

Using the Independent T-Test, result of Sig Levene’s Test would be firstly examined. If

Sig Levene’s Test is lower than 0.05, the variance is a different between two variables. Then the value of Sig T-test of Equal variances not assumed should be employed. In the case Sig Levene’s

Test is great than 0.05, the variance of two variables is not different. Then the value of Sig T-

Test of Equal variances assumed should be used. The value of Sig T-Test <0.05, it should be concluded that there are statistically significant differences between two variables. On the contrary, the value of Sig T-Test >0.05, two variables are statistically different.

4.6.1 Independent T-Test of Gender

The result of table 4.6.1 showed that Sig Levene’s Test 0.008 < 0.05. The value 0.127 of

Sig. T-Test was used. There was no difference between male and female on SNSS variable because Sig. T-Test 0.127 > 0.05. There is no effect of gender on SNSS. The Sig. Levene’s Test

0. 075 > 0.05. The value 0.511 of Sig. T-Test was used. There was no difference between male and female on EWOM variable because Sig. T-Test 0.511 > 0.05. There is no effect of gender on

EWOM. Because the Sig. Levene’s Test is 0.072 > 0.05, the value 0.028 of Sig T-Test was used.

There was a difference between male and female on IS variable because Sig. T-Test 0.028 < 0.05.

There is an effect of gender on IS. Because the Sig. Levene’s Test is 0.04 < 0.05, the value 0.187 of Sig T-Test was used. There was no difference between male and female on HI variable because Sig. T-Test 0.187 >0.05. There is no effect of gender on HI. Therefore, gender factor will not be supported.

Table 4.6.1 Independent T-Test of Gender

Variables Constructs

SNSS EWOM IS HI

Gender

Levene Statistic 0.008 0.75 0.72 0.04 Sig. Sig. (2 tailed) t- 0.127 0.511 0.28 0.187 test

Source: Drawn by the author from SPSS input data.

4.6.2 Independent T-Test of Marital Status

The result of table 4.6.2 showed that Sig Levene’s Test 0.582 > 0.05. The value 0.863 of

Sig. T-Test was used. There was no difference between married and unmarried on SNSS variable because Sig. T-Test 0.863 > 0.05. There is no effect of marital status on SNSS. Because the Sig

Levene’s Test is 0.482 > 0.05, the value 0.451 of Sig T-Test was used. There was no difference between married and unmarried on EWOM variable because Sig. T-Test 0.451 > 0.05. There is no effect of marital status on EWOM. Because the Sig Levene’s Test is 0.647 > 0.05, the value

0.145 of Sig T-Test was used. There was no difference between married and unmarried on IS variable because Sig T-Test 0.145 > 0.05. There is no effect of marital status on IS. Because the

Sig Levene’s Test is 0.952 > 0.05, the value 0.671 of Sig T-Test was used. There was no difference between married and unmarried on HI variable because Sig T-Test 0.671 > 0.05.

There is no effect of marital status on HI. Therefore, Marital Status factor will not be supported.

Table 4.6.2 Independent T-Test of Marital Status

Variables Constructs

SNSS EWOM IS HI

Marital Status

Levene Statistic 0.582 0.482 0.647 0.952 Sig. Sig. (2 tailed) t- 0.863 0.451 0.145 0.671 test

Source: Drawn by the author from SPSS input data.

4.6.3 Independent T-Test of Like Homestay

The result of table 4.6.3 showed that Sig Levene’s Test 0.312 > 0.05. The value 0.826 of

Sig. T-Test was used. There was no difference between yes and no on SNSS variable because

Sig. T-Test 0.826 > 0.05. There is no effect of Like Homestay on SNSS. Because the Sig

Levene’s Test is 0.119 > 0.05, the value 0.524 of Sig. T-Test was used. There was no difference between yes and no on EWOM variable because Sig. T-Test 0.524 > 0.05. There is no effect of

Like Homestay on EWOM. Because the Sig. Levene’s Test is 0.041 < 0.05, the value 0.209 of

Sig. T-Test was used. There was no difference between yes and no with IS variable because Sig.

T-Test 0.209 > 0.05. There is no effect of Like Homestay on IS. Because the Sig. Levene’s Test is 0.180 >0.05, the value 0.503 of Sig T-Test was used. There was no difference between yes and

no on HI variable because Sig T-Test 0.503 > 0.05. There is no effect of Like Homestay on HI.

Therefore, Like Homestay will not be supported.

Table 4.6.3 Independent T-Test of Like Homestay

Variables Constructs

SNSS EWOM IS HI

Like Homestay

Levene Statistic 0.312 0.119 0.41 0.18 Sig. Sig. (2 tailed) t- 0.826 0.524 0.526 0.503 test

Source: Drawn by the author from SPSS input data.

4.6.4 Independent T-Test of Efficiency of SNSS

The result of table 4.6.4 showed that Sig. Levene’s Test 0.214 > 0.05. The value 0.204 of

Sig. T-Test was used. There was no difference between yes and no on SNSS variable because

Sig. T-Test 0.204 > 0.05. There is no effect of Efficiency of SNSS on SNSS. Because the Sig

Levene’s Test is 0.896 > 0.05, the value 0.118 of Sig. T-Test was used. There was no difference between yes and no on EWOM variable because Sig. T-Test 0.118 > 0.05. There is no effect of

Efficiency of SNSS on EWOM. The Sig Levene’s Test 0.164 > 0.05. The value 0.419 of Sig. T-

Test was used. There was no difference between yes and no with IS variable because Sig. T-Test

0.419 > 0.05. There is no effect of Efficiency of SNSS on IS. Because the Sig Levene’s Test is

0.965 > 0.05, the value 0.300 of Sig. T-Test was used. There was no difference between yes and no on HI variable because Sig. T-Test 0.300 > 0.05. There is no effect of Efficiency of SNSS on

HI. Therefore, Efficiency of SNSS

Factor will not be supported.

Table 4.6.4 Independent T-Test of Efficiency of SNSS

Variables Constructs

SNSS EWOM IS HI

Efficiency of SNSS

Levene Statistic 0.214 0.896 0.164 0.965 Sig. Sig. (2 tailed) t- 0.204 0.118 0.419 0.30 test

Source: Drawn by the author from SPSS input data.

4.7 One way Anova Analysis

In One way Anova analysis, there are eight group features of demographic respondents employed to compare their means with four variables in the research model framework. They included Age, Educational Background, Occupation Status, Monthly Income, Living Place,

Frequency of using SNSS, Which SNSS, and Frequency of traveling. Each feature of these groups was analyzed to determine if there are any significant differences between the means towards each variable: Social Networking Sites, Electronic Words of Mouth communication,

Information Sharing, and Homestay Intention. Anova analysis results would be showed the significance. If the Sig is <5%, there is a difference mean in group towards the variable tested.

Otherwise, there is no difference with the Sig >5%.

4.7.1 One way Anova Analysis of Age

The result of the table 4.7.1 showed that Anova Test for Sig. = 0.112 > 0.05, it can be concluded that there is no difference among age group in SNSS variable. This means the age factor does not effect on SNSS. There is no influence of age on SNSS. The Anova Test for Sig. =

0.317 > 0.05, it can be concluded that there is no difference among age group in EWOM variable.

This means the age factor does not effect on EWOM. There is no influence of age on EWOM.

The Anova Test for Sig. = 0.297 > 0.05, it can be concluded that there is no difference among age group in IS variable. This means the age factor does not effect on IS. There is no influence of age on IS. The Anova Test for Sig. = 0.440 > 0.05, it can be concluded that there is no difference among Age group in HI variable. This means the Age factor does not effect on HI. There is no influence of age on HI. Therefore, age factor will not be supported. Table 4.7.1 Anova test of age

Variables Constructs SNSS EWOM IS HI

Age Levene Statistic Sig. 0.13 0.04 0.03 0.17 ANOVA F 1.897 1.189 1.235 0.943 Sig. 0.112 0.317 0.297 0.440

Source: Drawn by the author from SPSS input data.

4.7.2 One way Anova Analysis of Educational Background

The result of the table 4.7.2 showed that Anova Test for Sig. = 0.321 > 0.05, it can be concluded that there is no difference among Educational Background group in SNSS variable.

This means the Educational Background factor does not effect on SNSS. There is no influence of

Educational Background on SNSS. The Anova Test for Sig. = 0.08 > 0.05, it can be concluded that there is no difference among Educational Background group in EWOM variable. This means the Educational Background factor does not effect on EWOM. There is no influence of

Educational Background on EWOM. The Anova Test for Sig. = 0.178 > 0.05, it can be concluded that there is no difference among Educational Background group in IS variable. This means the Educational Background factor does not effect on IS. There is no influence of

Educational Background on IS. The Anova Test for Sig. = 0.042 < 0.05, it can be concluded that there is a difference among Educational Background group in HI variable. This means the

Educational Background factor effects on HI. There is an influence of Educational Background on HI. Therefore, Educational Background factor will be partially supported. Table 4.7.2 Anova test of Educational Background

Variables Constructs SNSS EWOM IS HI Education Background Levene Statistic Sig. 0.11 0.02 0.009 0.003 ANOVA F 1.180 2.119 1.591 2.521 Sig 0.321 0.08 0.178 0.042

Source: Drawn by the author from SPSS input data.

4.7.3 One way Anova Analysis of Occupation Status

The result of the table 4.7.3 showed that Anova Test for Sig. = 0.985 > 0.05, it can be concluded that there is no difference among Occupation Status group in SNSS variable. This means the Occupation Status factor does not effect on SNSS. There is no influence of

Occupation Status on SNSS. The Anova Test for Sig. = 0.772 > 0.05, it can be concluded that there is no difference among Occupation Status group in EWOM variable. This means the

Occupation Status factor does not effect on EWOM. There is no influence of Occupation Status

on EWOM. The Anova Test for Sig. = 0.904 > 0.05, it can be concluded that there is no difference among Occupation Status group in IS variable. This means the Occupation Status factor does not effect on IS. There is no influence of Occupation Status on IS. The Anova Test for Sig. = 0.876 > 0.05, it can be concluded that there is no difference among Occupation Status group in HI variable. This means the Occupation Status factor does not effect on HI. There is no influence of Occupation Status on HI. Therefore, occupation Status will not be supported.

Table 4.7.3 Anova test of Occupation Status

Variables Constructs SNSS EWOM IS HI Occupation Status Levene Statistic Sig. 0.561 0.46 0.224 0.457 ANOVA F 0.93 0.45 0.259 0.302 Sig 0.985 0.772 0.904 0.876

Source: Drawn by the author from SPSS input data.

4.7.4 One way Anova Analysis of Monthly Income

The result of the table 4.7.4 showed that Anova Test for Sig. = 0.903 > 0.05, it can be concluded that there is no difference among Monthly Income group in SNSS variable. This means the Monthly Income factor does not effect on SNSS. There is no influence of Monthly

Income on SNSS. The Anova Test for Sig. = 0.863 > 0.05, it can be concluded that there is no difference among Monthly Income group in EWOM variable. This means the Monthly Income factor does not effect on EWOM. There is no influence of Monthly Income on EWOM. The

Anova Test for Sig. = 0.743 > 0.05, it can be concluded that there is no difference among

Monthly Income group in IS variable. This means the Monthly Income factor does not effect on

IS. There is no influence of Monthly Income on IS. The Anova Test for Sig. = 0.846 > 0.05, it

can be concluded that there is no difference among Monthly Income group in HI variable. This means the Monthly Income factor does not effect on HI. There is no influence of Monthly

Income on HI. Therefore, monthly income factor will not be supported.

Table 4.7.4 Anova test of Monthly Income

Variables Constructs SNSS EWOM IS HI Monthly Income Levene Statistic Sig. 0.528 0.666 0.899 0.723 ANOVA F 0.316 0.379 0.543 0.403 Sig 0.903 0.863 0.743 0.846

Source: Drawn by the author from SPSS input data.

4.7.5 One way Anova Analysis of Living Place

The result of the table 4.7.5 showed that Anova Test for Sig. = 0.22 > 0.05, it can be concluded that there is no difference among Living Place group in SNSS variable. This means the Living Place factor does not effect on SNSS. There is no influence of Living Place on SNSS.

The Anova Test for Sig. = 0.071 > 0.05, it can be concluded that there is no difference among

Living Place group in EWOM variable. This means the Living Place factor does not effect on

EWOM. There is no influence of Living Place on EWOM. The Anova Test for Sig. = 0.043 <

0.05, it can be concluded that there is a difference among Living Place group in IS variable. This means the Living Place factor effects on IS. There is an influence of Living Place on IS. The

Anova Test for Sig. = 0.107 > 0.05, it can be concluded that there is no difference among Living

Place group in HI variable. This means the Living Place factor does not effect on HI. There is no influence of Living Place on HI. Therefore, living place factor will be partially supported.

Table 4.7.5 Anova test of Living Place

Variables Constructs SNSS EWOM IS HI Living Place Levene Statistic Sig. 0.50 0.142 0.82 0.829 ANOVA F 2.912 2.192 2.504 1.928 Sig 0.22 0.071 0.043 0.107

Source: Drawn by the author from SPSS input data.

4.7.6 One way Anova Analysis of Frequency of Using SNSS

The result of the table 4.7.6 showed that Anova Test for Sig. = 0.816 > 0.05, it can be concluded that there is no difference among frequency of using SNSS group in SNSS variable.

This means the frequency of using SNSS factor does not affect SNSS. There is no influence of frequency of using SNSS on SNSS. The Anova Test for Sig. = 0.786 > 0.05, it can be concluded that there is no difference among frequency of using SNSS group in EWOM variable. This means the frequency of using SNSS factor does not affect EWOM. There is no influence of frequency of using SNSS on EWOM. The Anova Test for Sig. = 0.812 > 0.05, it can be concluded that there is no difference among frequency of using SNSS group in IS variable. This means the frequency of using SNSS factor does not affect IS. There is no influence of frequency of using SNSS on IS. The Anova Test for Sig. = 0.781 > 0.05, it can be concluded that there is no difference among frequency of using SNSS group in HI variable. This means the frequency of using SNSS factor does not affect HI. There is no influence of frequency of using SNSS on HI.

Therefore, frequency of using SNSS will not be supported.

Table 4.7.6 Anova test of Frequency of Using SNSS

Variables Constructs

SNSS EWOM IS HI Frequency of Using SNSS Levene Statistic Sig. 0.151 0.63 0.28 0.176 ANOVA F 0.390 0.431 0.396 0.438 Sig 0.816 0.786 0.812 0.781

Source: Drawn by the author from SPSS input data.

4.7.7 One way Anova Analysis of Which SNSS

The result of the table 4.7.7showed that Anova Test for Sig. = 0.247 > 0.05, it can be concluded that there is no difference among which SNSS group in SNSS variable. This means the SNSS factor does not affect SNSS. There is no influence of which SNSS on SNSS. The

Anova Test for Sig. = 0.139 > 0.05, it can be concluded that there is no difference among which

SNSS group in EWOM variable. This means the SNSS factor does not affect EWOM. There is no influence of which SNSS on EWOM. The Anova Test for Sig. = 0.122 > 0.05, it can be concluded that there is no difference among which SNSS group in IS variable. This means the

SNSS factor does not affect IS. There is no influence of which SNSS on IS. The Anova Test for

Sig. = 0.154 > 0.05, it can be concluded that there is no difference among which SNSS group in

HI variable. This means the SNSS factor does not affect HI. There is no influence of which

SNSS on HI. Therefore, the variable of “which SNSS” will not be supported. Table 4.7.7 Anova test of Which SNSS

Variables Constructs SNSS EWOM IS HI Which SNSS Levene Statistic Sig. 0.712 0.817 0.866 0.306 ANOVA F 1.365 1.755 1.839 1.688 Sig 0.247 0.139 0.122 0.154

Source: Drawn by the author from SPSS input data.

4.7.8 One way Anova Analysis of Frequency of travelling

The result of the table 4.7.8 showed that Anova Test for Sig. = 0.307 > 0.05, it can be concluded that there is no difference among frequency of travelling group in SNSS variable. This means the frequency of travelling factor does not effect on HI. There is no influence of frequency of travelling on HI. The Anova Test for Sig. = 0.434 > 0.05, it can be concluded that there is no difference among frequency of travelling group in EWOM variable. This means the frequency of travelling factor does not effect on EWOM. There is no influence of frequency of travelling on EWOM. The Anova Test for Sig. = 0.179 > 0.05, it can be concluded that there is no difference among frequency of travelling group in IS variable. This means the frequency of travelling factor does not effect on IS. There is no influence of frequency of travelling on IS. The

Anova Test for Sig. = 0.085 > 0.05, it can be concluded that there is no difference among frequency of travelling group in HI variable. This means the frequency of travelling factor does not effect on HI. There is no influence of frequency of travelling on HI. Therefore, frequency of traveling factor will not be supported. Table 4.7.8 Anova test of frequency of travelling

Variables Constructs SNSS EWOM IS HI Frequency of travelling Levene Statistic Sig. 0.106 0.118 0.145 0.262 ANOVA F 1.210 0.915 1.650 2.232 Sig 0.307 0.434 0.179 0.085

Source: Drawn by the author from SPSS input data.

Table 4.7.9 Summary of personal demographic characteristics

Hypotheses of personal Characteristics Status

H6: Personal demographic characters will influence on SNSS, EWOM Not supported communication, IS and HI.

H6.1 Gender factor will influence on SNSS, EWOM communication, IS and HI. Not supported

H6.2 Marital status factor will influence on SNSS, EWOM communication, IS and Not supported HI.

H6.3 Age factor will influence on SNSS, EWOM communication, IS and HI. Not supported

H6.4 Educational Background factor will influence on SNSS, EWOM Partially communication, IS and HI. supported

H6.5 Occupation Status factor will influence on SNSS, EWOM communication, Not supported IS and HI.

H6.6 Monthly income factor will influence on SNSS, EWOM communication, IS Not supported and HI.

H6.7 Living place factor will influence on SNSS, EWOM communication, IS and Partially HI. supported

H6.8 Frequency of using SNSS factor will influence on SNSS, EWOM Not supported communication, IS and HI.

H6.9 Which SNSS factor will influence on SNSS, EWOM communication, IS and Not supported HI.

H6.10 Frequency of traveling factor will influence on SNSS, EWOM Not supported communication, IS and HI.

H6.11 Like homestay factor will influence on SNSS, EWOM communication, IS Not supported and HI.

H6.12 Efficiency of SNSS factor will influence on SNSS, EWOM Not supported communication, IS and HI.

Chapter 5: Conclusion and recommendations

This study research was conducted to give the answer on the question how social networking sites affected on homestay intention. After using the online and offline survey with questionnaire through Facebook, and based on responses of 220 selected respondents, the author applied the SPSS software to analyze the data collected. The results of research analysis mentioned in chapter 4 showed us the certain statistical significance of the data to find out which previously stated hypotheses accepted or rejected. There existed positive relationships between four variables of the research. In other words, this paper showed the influence of social networking sites on homestay intention with two moderators; electronic word of mouth communication, and information sharing from social networking sites. Thus, with five positive hypotheses of research were supported, the main objective of this paper was clarified.

5.1 Mai findings and discussion

The results of the research analysis steps in chapter 4 showed us certain values and evidence to get closer initial objective of this research. All three factors of constructed research model affect homestay intention. Only personal character of respondents partially influenced on all four variables of this research. In this part below, some main findings and discussion were drawn to reconfirm a positive relationship of social networking sites, electronic word of mouth communication, information sharing and homestay intention.

5.1.1 Social networking sites

In this paper, social networking site was put into the empirical research model as a factor which has a direct effect on remaining factors, electronic word of mouth, information sharing and homestay intention.

By using single regression analysis, the author discovered that social networking sites had definite effects on electronic word of mouth communication. In particular, the values of regression analysis of EWOM on SNSS (β = 0.871, t = 26.134, p =0.000) means that SNSS makes EWOM increase 0.871 unit when it has one unit risen. In chapter 2, the relationship between SNSS and EWOM communication was mentioned. Thanks to SNSS, EWOM communication has become an informative and reliable source for customers searching and sharing information of products. For marketers, EWOM communication on SNSS has been considered as a proficient efficient marketing tool to approach closely and successfully their customers. And customers are also quickly updated all customized products in need if they engaged with EWOM communication. In this case, EWOM communication on SNSS will help people and travelers have a good platform to satisfy their engagement. And homestay travelling will be a more popular optional which hosts and travelling companies offer to people.

After using regression analysis of information sharing with social networking sites, the result of this step was showed that IS will increase 0.905 unit if SNSS increases one unit because of these values (β = 0.905, t = 31.326, p = 0.000). This proved that there existed a positive effect of SNSS on IS. Nowadays, information sharing on SNSS has turned essential needs of social network users. Especially, people have enjoyed their travelling activities and information shared on SNSS while and after their vacations. So, information sharing on SNSS has been regarded as an effective source for people meeting their demands. If homestay sites or any information related to homestay traveling activities are shared on SNSS by social network users. They will be

a trustworthy information source for their relatives, friends and acquaintances. When they post their photos, videos, recommendations and homestay destinations, social network users who are delighted will be excited and want to have these experiences. Then, homestay will become well- chosen traveling type for people since this information shared on SNSS.

5.1.2 Homestay intention

Homestay intention factor was tested with three factors including social networking sites, electronic word of mouth communication, and information sharing by applying regression analysis. This analysis put out the values below.

The β = 0.876, t = 26.864, p = 0.000 are the results from regression analysis of homestay intention on social networking sites. Thus, the equation in chapter 4 indicated that HI will increase 0.876 units when SNSS increase one unit. This means SNSS will have a certain influence on HI. As presented in literature review, SNSS has been an impactful platform for customers and leading them to become buyers after having strong purchase intention with concerning products. The findings of this research discovered homestay intention will be meaningfully grown up with support of SNSS.

When using regression analysis of homestay intention on electronic word of mouth communication, the author got the values with β = 0.868, t = 25.793, p = 0.000. These values helped this research find out a confirming influence of HI on EWOM. When EWOM changes one unit in larger volume, the HI also increasingly changes 0.868 units. Therefore, some discussion for two these factors should be stated that thanks to EWOM communication, people who are have intention of homestay will easily satisfy their concerns with this type of travelling.

That is because EWOM communication will be able to be a good reference for them to engage

their homestay products. In other words, EWOM communication might be considered as a factor that can lead homestay intention to become purchasing action by travelers.

The third factor which homestay intention was influenced when using regression analysis is information sharing because of these values (β = 0.889, t = 28.609, p = 0.000). According to the results, if IS increase one unit, HI will increase 0.889 units. It is an assertive influence which

IS has an accurate impact on HI. It can be seen in this paper, information sharing contributed a considerable part to homestay intention, becoming popular source with people who shares and to be shared homestay travelling information on SNSS.

5.1.3 Personal characters

One way Anova and Independent T-Test applied in chapter 4 added some findings correlation of personal demographic of respondents with four constructs built in research model.

The results of these analysis methods showed that all most factors of personal characters have no influence on four constructs of the research. Particularly, gender factor, and two groups were tested, male and female, will not influence on social networking sites, electronic word of mouth communication, information sharing, and homestay intention. Marital status also has no any effects on social networking sites, electronic word of mouth communication, information sharing, and homestay intention whether respondents belong to married or unmarried group. Follow that, educational background, occupation status, monthly income, and living place factors will not be supported when including in this research with four variables proposed. There is a clear mark proving no difference in each group of these factors even from different group in choice of distinguish answers. However, educational background has an influence on homestay intention.

And living place also influence on information sharing.

With four extended questions to test frequency of using SNNS, and frequency of traveling, which SNSS respondents often using, and interests in homestay intention, the results of

One way Anova and Independent T-Test analysis also gave out the same finding. This means four these factors have no influence on social networking sites, electronic word of mouth communication, information sharing, and homestay intention. From the perspective of this paper, even though people using different SNSS, different frequency of traveling, like or dislike homestay travelling, but they have the same views. Therefore, there is no influence on social networking sites, electronic word of mouth communication, and information sharing, and homestay intention with these factors.

5.2 Contribution

This paper was conducted and its some main findings were drawn out with desire to contribute a small support to future researches. In future, this topic will be studied further and boarded if someone wants to develop it when some basic features are found in this paper.

Towards closer research objective, main findings will be considered a good resource for the host of homestay destinations realizing some good effects of social networking sites, and then they can develop and introduce their homestay sites to social network users and people who love homestay traveling around the world.

Homestay is an efficient and outlook form in tourism industry. Hopefully, this paper will arouse more interests, attention and concerns this type of travel. More investments will be conducted to expand and develop becoming a key form in tourism industry, contributing conservation ecosystem for tourism, creating a distinctive feature of tourism of Vietnam.

5.3 Limitations and recommendations for future researches

Because of limitation of time and finance, the survey was carried out in short time, 30 days, and posted through Facebook. And the sample was selected in small size, and respondents are the author’s friends and acquaintances in the same generation, hence inevitable same ideas when giving answers for the questionnaire. In order to help future researches have a better and further study if some of findings of this paper would be used, some limitations and recommendations was discussed in this part.

The first limitation of this paper is small sample size due to data collected from relative participants in small number. Meanwhile, a larger sample will have more accurate results because the data in larger samples will reflect opinion of population. Therefore, the sample size of other future researches should be increased in larger to gather big data for data more accurately analyzed.

The second one which the author limited this paper was the language of the questionnaire, only in Vietnamese and focused on participants from one nationality. So, if some future researches in the same topic are studied in many languages with huge respondents from many other countries, the results will be different and have more findings.

The third limitation of this paper is model research. The author found out that empirical analysis was limited about moderating variables effecting on homestay intention. In future, this topic will be improved with more moderators by later researchers. And each variable also needs to be full and more particular. For instance, information sharing should be included more informative like price, service, and some specific features, ect. of homestay place shared on

SNSS. Thus, the author highly recommends future researchers to test separately information

sharing features as well each social networking site. This maybe help another studies get more results to show distinct influence of each variable.

Finally, the questionnaire of this paper was still limited by the author’s ability. An important recommendation for future researches, the questionnaire should be further developed to collect more adequate data.

5.4 Conclusion

The primary purpose of this paper was to learn the impact of common social networking sites on homestay intention in Vietnam by testing electronic word of mouth communication and information sharing variables built as the constructs of the research. The questionnaire was created in online and offline survey via the link on Facebook, and data was collected to be analyzed by applying SPSS software to gain the proposed structural model research. And five hypotheses of the model were tested to find out some reliable evidence regarding effects of social networking sites on homestay intention. In this thesis, there existed the correlation of social networking sites, electronic word of mouth communication, an information sharing with homestay intention.

Furthermore, the result and findings proved that there was a convinced significant influence of social networking sites on homestay intention. Therefore, a strong conclusion of this paper should be stated that people and travelers are able to feel comfortable to search and book a homestay holiday through social networking sites.

In addition, social networking sites for homestay will create a massive travel network by homestay travelers. This will be a huge platform for people who love this travelling form to connect each other. They are easy to connect with hosts and previous travelers to collect useful

information for their planning homestay holiday. Adding that, they also freely share their valuable photos and videos while traveling, and their recommendations will be an affirmative trustworthy source for their relative and friends. This means everyone in this platform can help each other to save money and time for searching and planning an expected homestay traveling.

Through using social networking sites for homestay traveling, tourism industry will be expanded and established more activities with this form to meet demand today.

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APPENDIX

QUESTIONNAIRE

A. PERSONAL INFORMATION

1. Gender： male female

2. Marital status： unmarried married

3. Age：

under 20 21-30

31-40 41-50

above 51

4.Educational Background：

Junior high school Senior high school

College University

Master  Doctor

5.Occupation Status:

 Full-time work  Student

 Part-time work  Other:

 Self-employed

6.Monthly Income (VND)

Under 6 millions  6 millions – 10 millions

10 millions – 15 millions  15 millions– 20 millions

20 millions – 25 millions  Above 25 millions

7.Living Place:

 Metropolis  Town

 City  Suburb

 Countryside  Coastal areas

8. How frequently do you use the social networking sites?

 Daily  2 – 6 times a week

 Once a week  Once a month

 Other

9. Which social networking sites do you most commonly use to get information about travelling?

Facebook Instagram

You tube Google+

Other

10. How often do you go travelling per year?

1- 3 times 4 -6 times

7- 10 times Over 10 times

11. Do you like homestay travelling?

yes  No

12. Do you think it is efficient when booking homestay vacation on social networking sites?

yes No

B. HOMESTAY INTENTION

Degree Strongly Strongly Disagree Neutral Agree Questions disagree agree

I. Social networking site

1. SNSS have enabled me to create, publish, form groups, and share homestay travelling interests, exchange opinions or suggestions.

2. SNSS provide platforms for me to gratify my status and information seeking needs by sharing information. 3.Thanks to SNSS, I can effectively collect full information of homestay destinations I need. 4. Using social networking sites help me make decisions better before purchasing homestay travelling. 5. SNSS have become popular platforms for me to share my photos and videos of my travelling experience. 6. SNSS play an important role in information diffusion among tourist and influence my intention.

7. SSNS is an effective informative source for supporting me in planning homestay vacations.

8. SNSS enable me to stay in touch with people linked to access homestay sites. II. EWOM

communication 1. Like other consumers, I can engage in EWOM communication through social networking sites in varied degrees.

2.EWOM communication on social networking sites helps in increasing interest of my homestay travelling and providing me customized information.

3.I considered EWOM communication as a type of social influence that affects my belief, attitude, and homestay intention.

4.EWOM communication has a positive impact on my purchasing intention, especially homestay travelling products.

5.I trust EWOM communication platform as a consultant before having homestay intention.

6.Using the EWOM communication, I can get much

more interaction with other consumers and get faster response about the homestay product information.

III. Information Sharing

1. I trust information sharing of homestay travelling on SNSS shared by my relatives and friends.

2. I consider information sharing of homestay travelling on SNSS from my relatives and friends to plan for my future vacations.

3. I prefer to make an informed purchase decision by collecting as much information as I can get through social networking sites.

4.Video-sharing on social networking sites, make me want to visit homestay I have already seen in those videos.

5. Shared photos on social networking sites, make me want to visit homestay I have already seen in those photos.

6. I can get several options of homestay destinations shared by other users on SNSS.

7. Using social networking sites to get recommendations from acquaintances are affecting my decision-making on homestay travelling.

IV. Homestay Intention

1. Using social networking sites of homestay help me make decisions better before booking homestay holiday.

2. Using social networking sites of homestay increase my interest in homestay travelling.

3. I will choose homestay for next holiday after reviewing a good homestay site.

4. I am very likely to purchase homestay products recommended by my friends on social networking sites

5. I intend to purchase homestay travelling on social networking sites, I follow.

6. I expect to purchase homestay products on social networking sites, I follow.